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Next filing · Form 10-Q · Q2 2026 · 54 daysFactor Research · Part 2 published: risk structure in 13F filings across five allocator stylesAPI Update · AOM portfolio chains — single snapshot call for multi-step analyze flowsAPI Update · POST /api/snapshot — canonical JSON portfolio snapshotPart 3 · The One Manager Skill That PersistsPart 1 · One Position, Four BetsNext filing · Form 10-Q · Q2 2026 · 54 daysFactor Research · Part 2 published: risk structure in 13F filings across five allocator stylesAPI Update · AOM portfolio chains — single snapshot call for multi-step analyze flowsAPI Update · POST /api/snapshot — canonical JSON portfolio snapshotPart 3 · The One Manager Skill That PersistsPart 1 · One Position, Four Bets
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    • Cascade Hedging and the Cost of Interpretability
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    • ERM3 Cascade-Residual Persistence and the Allocator Skill Signal
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Concept · The RiskModels engine (ERM3)

Replication equation

The raw-ETF identity that reproduces a stock's return from its hedge legs — only tradeable ETF returns, no orthogonalization at trade time. Verified to ±0.1% for every stock, every date.

In depth

Reproduces a stock's return from its hedge legs using only raw, tradeable ETF returns — no orthogonalization needed at trade time. Verified to ±0.1% for every stock on every date.

Compute it with the API

POST /api/decompose

# pip install riskmodels-py
client.decompose("NVDA")  # returns hedge legs

Full API docs ↗

In the methodology

Putting it together: the replication equation →

Referenced by (0)

No working paper references this concept yet.

Related concepts

ERM3Hierarchical cascade (L1/L2/L3)Link beta (λ)Explained risk (ER)Robust beta (Huber-M)Vasicek shrinkageL-star ruleGeometric attribution bridgeResidual mean-reversion signal
← Explained risk (ER)The RiskModels engine (ERM3) · 5 / 10Robust beta (Huber-M) →
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